In the last few years, many machine learning techniques have been developed in order to classify data, such as decision tree, artificial neural network or support vector machine. They all work with the entire training set available and in most of cases it has to be saved in main memory. However, nowadays, every device connected to the Internet generates an ever growing stream of data (formally unbounded). Machine Learning on unbounded data streams is a grand challenge. In fact, standard techniques are not able to continuously incorporate new data. Consider, for example, a system predicting future free parking places in a silo based on the actual number of free parking places and on the number of people in the near square. Some Internet of Thing (IOT) sensors, every few seconds, register a new sample and send it right to a server. At server side, there must be an algorithm able to train a model with only a single sample at a time. Moreover, the statistics of such data are subject to gradual or sudden changes without any warning. This phenomena is known as concept drift [1]} and requires an algorithm that can adjust quickly to hanging conditions [2]}. Massive Online Analysis (MOA) is a library that contains new learners that are able to manage concept drifthttps://moa.cms.waikato.ac.nz/.
